GIS MIDTERM

Vector model

line models that store x, y, z locations as the primary unit; high precision

Raster models

stored in pixels, every cell has its own value; takes lots of storage

Vector Data

points, lines, polygons

Feature Class

a collection of similar objects with the same attributes (either points, lines, or polygons), can only have one type of data (ex: states; cities)

Geodatabases and feature datasets

allow you to organize multiple feature classes by theme (think folders in a typical computer structure) 

Raster Data Model 

You have to record something about every space

resolution

the more pixels or cells you have, the more useful information you have

Map scale

the ratio of distance on the map to distance on the ground; its dimentionless and can be expressed in any units (cm, in)

Map Scale

small scale cover large area (small detail) (1/50,000,000) (small fraction)

large scale cover small area (1/5,000) (large fraction)

logical consistency

asks if vector models match the real world (do 2 streets meet/ and or cross)

Changes in category

varying symbol shape, line type, pattern, color, or font

Changes in quantity

varying symbol size, thickness, or color

RBG

red, blue, green - scales from 0-255

0= black 255= white 

Hex Value

code for a number that you can type in and instantly receive that color

single symbol map type

used for nominal (categorical) data; shape scale/size, color psychology

unique value map type

used for categorical and ordinal data; graduated colors (continuous data, like heat) and graduated symbols map

nominal data

nominal (name) data names or uniquely identify objects (county names, airport names, parcel id numbers) 

each feature likely to have its own value

usually portrayed on a single symbol map w/ optimal labels

categorical data

features belong to categories (rock type, highway class, or land cover class)

names may be text or numeric

portrayed with a unique values map

ordinal data

type of categorical data; ranked but not numerical; ranks categories along arbitrary scale (ex: snail habitat (0)-unsuitable, (1) marginal, (2) acceptable, (3) ideal)

uses unique values map with a single-hue color scheme

interval data

places values along a regular numeric scale

if it can have negative values, its interval or ratio data (kelvin would be ratio; celsius & Fahren. would be interval) 

uses color ramp that increases color value or saturation

ratio data

places values along a regular scale with a meaningful zero point (zero means there is none)

population can’t have negative values, so its this type of data 

MAUP

modifiable area unit problem; arbitrary aggregation units like states or counties may influence values, maps reflect the influence rather than the data being mapped (# of farms in a state is affected by the size of state, # of vacant homes in a state is affected by pop of state)

Minimizing MAUP

normalizing (dividing) data by a suitable field allows data patterns to emerge (farms per square mile instead of # of farms)

Discrete Data (raster type)

represent objects such as roads or land use polygons, take on relatively few values, adjacent cells often have the same values, values may change abruptly at boundaries

Continuous data (raster type)

represent a measurement that occurs everywhere, gradual change, thousands or millions of potential values, few adjacent cells have same values, values may change rapidly from cell to cell

Thematic Rasters

contain quantities that represent map data, such a geology or elevation

• categorical/ ordinal rasters use unique values or discrete color display

• interval/ratio rasters use classified or stretched display methods

Stretching 

reallocates a smaller portion of the raster values to the 256 shades of the color scheme, improves brightness and contrast

different types (min-max or standard deviation)

image rasters

contains satellite or air photo data and generally represent brightness

displayed using the stretched method for single-band rasters or the RBB composite method for multiband rasters 

classifying data

divides data values in class ranges, each w/ own symbol, applies to both vector and raster maps

Jenks Natural Breaks classification

exploits natural gaps in the data, good for unevenly distributed or skewed data, default method that works well for most data sets

Defined interval or equal interval classification

methods for producing equally sized classes, (user chooses the class range for defined, user chooses the number of classes for equal) 

GPS

constellation of Earth-orbiting satellites maintained by the US gov. for the purpose of defining geographic positions on and above the surface of the Earth

Triangulation

works the same as GPS except it’s about spheres and distances (not lines and directions)

Each GPS satellite has a very precise time (atomic clock), GPS receivers use this signal to measure

Sources of GPS error

• clock error - differences between satellite and receiver clocks

• ionosphere delays - delay of GPS signals as they pass through the layer of charged ions and free electrons known as the ionosphere

• Multipath error - caused by local reflections of the GPS signal that mix with the desired signal (concrete, buildings, etc, can block the signal) 

Latitude

measures distance north or south of the equator, with lines running horizontally (east-west)

Longitude

measures distance east or west of the Prime Meridian, with lines running vertically (north-south)

Layout

includes one or more map frames, plus map elements like titles, legends, scale bars, etc

Datum

uses specific spheroid and translation to achieve the best possible fit between the Earth’s geoid and the mapping spheroid 

• local __ = optimized for a country or continent, or local area 

• world __ = optimized for the entire globe 

Projections

a mathematical model for converting locations on the earth’s surface from spherical to planar (flat) coordinates, allowing flat maps to depict 3D features 

Cylindrical Projection

distortion is absent where the cylinder touches the globe (tangent) and increases as you move away from that point 

typically preserve shape and direction at the expense of area and distance 

(mercator- preserves shape and direction but not area- used for nav.) good for tropical regions

Conic projections

distortion is absent at the standard parallels and increases as you move away from those lines; preserve area or distance and lose direction and shape (ex: tangent conic and secant conic) good for mapping mid equator

Azimuthal (orthographic) projections 

distortion is absent where the plane touches the globe and increases as you move away from that point, preserve area or distance and lose direction and shape, good for mapping poles

UTM (universal transverse mercator)

the world is divided into 60 zones, 6 degrees wide, distortion is minimal within each zone, best for maps covering small area in one zone 

State Plane System

States divided into one or more zones identified by a unique FIPS number

Define Projection Tool

creates or changes only CS label, doesn’t change the coordinates in the file. Keep the original data set, only use when CS is missing or incorrect 

Multipart feature

one feature that contains several separate pieces (ex: Hawaii)

Feature Classes

collection of similar features stored together as a data set 

linked to tables containing data, or attributes, of the features

unique integer is used to link the feature to its data record 

Shapefiles

type of spaghetti model, used to transfer data from one GIS to anotehr

Topological Models

store information on how features are spatially related, can test whether features are adjacent, connected overlap, or intersect

Geodatabases

use topological model, contains data like feature classes, rasters, tables, etc.

Metadata

data about a dataset that helps the user assess its purpose and quality 

travels w/ the data as a separate file or as part of the feature class

Importing and Exporting

makes a copy of data set

• you can bring a shapefile into a geodatabase, and it will become a feature class; you can select certain attributes and create a different feature class 

Merging and Appending

merge: two similar types of features are combined, the resulting table is a combo of the 2 original tables

append: adds additional features to an existing data set 

dissolving 

removes boundaries of features with the same value in the specified attribute field(s) 

Raster Model

raster stores an n*m array of values in cells or pixels representing squares on the ground: it’s georeferenced to an Earth location by an x-y in the corner and a specified cell size

• better at storing certain data: continuous, photos, etc

• Coordinate precision is generally lower; cant store multiple attributes

Raster Pyramids

may be built for a raster to speed its display; increases storage size of raster by 50%

Voxel

raster cell with height, encompassing a volume. Stacked together to store data values in 3D

Point Clouds

store millions of x,y,z triplets; often colored by elevation to visualize height

TINS (triangular irregular network)

stores x,y,z triplets (nodes) that define the corners of triangular facets bounded by linear eddges; efficiently store 3D surfaces by using fewer nodes for flat areas and more nodes for steep areas 

Attribute Table

stores attributes of map features, associated with a spatial data layer, has special fields for spatial information

standalone tables

stores any tabular data, not associated with spatial data, can be brought from text files, spreadsheets, GPS files, database files, excel 

Joining Tables

allow two tables to be used as a single table; target receives the additional information (usually feature class attribute table) and the join provides the additional information (usually standalone table) 

Cardinality of Joins

relationship of table (target to join)

• many to one

• one to one 

each record in the table must match one and only one record in the join table

Data type

short, long, float (decimals), double (large decimal), text, date, blob (photos, documents, etc) 

Coded domain

provides lists of values to pick from

Range domains

specify the range of numeric values permitted

Summary Statistics

simple: get stat on one variable

summarized: use a “case field” to get statistics by subfield